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在极端高温冲击下使用实验和虚拟数据预测离子电池的热逃逸温度

Xiaoyu Li¹, Shen Zhao¹, Xinyu Wei¹

¹School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|September 24, 2025

概括

这项研究引入了一个新的框架,用于预测离子电池在极端高温下的热失控温度. 它结合了实验和模拟数据,改善了高能电池的安全预测.

科学领域:

材料科学材料科学材料科学
化学工程是化学工程的重要组成部分.
电池技术电池技术

背景情况:

离子电池 (LIB) 的能量密度增加加剧了热安全问题.
传统的测热方法不能准确地模拟现实世界的火灾场景,如火焰暴露.
准确的热失控 (TR) 预测对于LIB安全至关重要.

研究的目的:

开发一个框架来预测在极端高温冲击条件下LIBs的热失控温度.
结合实验和虚拟数据,提高TR预测的准确性.
在冲击波引起的加热下研究NCM523和LFP电池的TR行为.

主要方法:

对NCM523和LFP LIB进行了高温冲击波诱导的TR测试.
开发了一种3D联合热传递和TR合模拟模型.
使用模拟模型为各种电荷状态 (SOC) 和热源距离生成虚拟TR数据.
采用深度学习模型来预测TR温度变化.

主要成果:

模拟模型准确地捕获了实验TR的温度参数.
虚拟数据生成补偿了有限的实验数据,使得数据驱动的预测.
深度学习模型在不同的SOC和热源距离下成功预测了TR温度趋势.
最佳预测框架实现了低于5%的平均绝对百分比误差 (MAPE).

结论:

拟议的框架有效地整合了实验和虚拟数据,用于在LIBs中高准确性,快速响应的TR温度预测.
这种方法提高了在极端热条件下LIBs的安全性评估.
数据驱动的预测模型提供了一种可靠的方法来评估不同操作状态和环境因素的电池安全性.

关键词:

深度学习是一种深度学习.高温冲击是一种高温冲击.离子电池是一种离子电池.充电状态充电状态温度预测温度预测热逃跑模型的热逃跑模型

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